Machine learning approach for radiographic non-destructive testing

Abstract

A machine-learning model is trained using images of structures including defects and images of structures not including defects. Preprocessing is performed on the images before training the machine-learning model. The trained machine-learning model is used to classify defects within images of structures. Images of structures with defects are identified, and the probabilities of the identification/defect classification are obtained.

What is claimed is: 
     
         1 . A system for radiographic non-destructive testing, the system comprising:
 one or more physical processors configured by machine-readable instructions to: 
 obtain training information, the training information defining training images of a structure and labeling of the training images as including the structure with a defect or as including the structure without the defect; 
 train a machine-learning model using the training images of the structure and the labeling of the training images, wherein the trained machine-learning model provides classification of input images as including the structure with the defect or as including the structure without the defect and probability of the classification of the input images; 
 obtaining image information, the image information defining an image of the structure; and 
 determine classification of the image of the structure as including the structure with the defect or as including the structure without the defect and determine probability of the classification of the image by inputting the image into the trained machine-learning model. 
   
     
     
         2 . The system of  claim 1 , wherein the training images of the structure includes x-ray images or gamma ray images of the structure. 
     
     
         3 . The system of  claim 1 , wherein the training images are preprocessed before the machine-learning model is trained. 
     
     
         4 . The system of  claim 3 , wherein the preprocessing of the training images includes changing dimension of one or more of the training images. 
     
     
         5 . The system of  claim 4 , wherein the dimension of the one or more of the training images is changed based on rotation, padding, stretching, and/or cropping of the one or more of the training images. 
     
     
         6 . The system of  claim 3 , wherein the preprocessing of the training images includes changing size of one or more of the training images. 
     
     
         7 . The system of  claim 3 , wherein the preprocessing of the training images includes changing exposure and/or color scale of one or more of the training images. 
     
     
         8 . The system of  claim 1 , wherein the machine-learning model includes a convolutional neural network with multiple convolutional layers and a fully connected layer. 
     
     
         9 . The system of  claim 1 , wherein the machine-learning model includes a residual neural network that requires a single-channel input. 
     
     
         10 . The system of  claim 1 , wherein the classification of the input images as including the structure with the defect further includes classification of a type of defect within the input images and/or identification of a location and/or a size of the defect. 
     
     
         11 . The system of  claim 1 , wherein the training images of the structure are divided into subsets using multi-class stratification, individual subsets representing different types of defects. 
     
     
         12 . The system of  claim 1 , wherein the machine-learning model is pretrained, and training of the machine-learning model using the training images of the structure and the labeling of the training images includes fine-tuning the machine-learning model using the training images of the structure and the labeling of the training images. 
     
     
         13 . The system of  claim 1 , wherein memory requirement for weights of the machine-learning model is reduced in training using automatic mixed precision. 
     
     
         14 . The system of  claim 1 , wherein memory requirement for training of the machine-learning model using the training images of the structure is reduced using gradient accumulation. 
     
     
         15 . The system of  claim 1 , wherein the machine-learning model is trained deterministically.